Optimizing a virtualized computing environment

ABSTRACT

A method and associated systems for optimizing a computing platform. A processor joins sets of configurable parameters into groups that each identifies a configuration of the computing environment or of a component or subsystem of the computing environment. The processor generates a set of variations of each group, where each variation identifies a candidate configuration of the component, subsystem, or platform, and where each candidate configuration identifies a distinct set of values of the group of parameters associated with that component, subsystem, or platform. Each configuration of this first generation of configurations undergoes a massively parallel iterative procedure that generates a next generation of configurations by performing operations upon the first generation that are similar to those of a natural-selection process. The procedure repeats until successive generations converge, within resource constraints, to a fittest generation that represents an optimal or most nearly optimal configuration of the computing platform.

This application is a continuation application claiming priority to Ser.No. 15/602,213, filed May 23, 2017, now U.S. Pat. No. 9,852,043, issuedDec. 26, 2017, which is a continuation of Ser. No. 14/458,403, filedAug. 13, 2014, U.S. Pat. No. 9,734,036, issued Aug. 15, 2017.

TECHNICAL FIELD

The present invention relates to optimization of computer resources.

BACKGROUND

A computing platform, such as an enterprise network or a virtualizedcloud-computing environment, may comprise an enormous number ofconfigurable components, and the performance of each component may be afunction of numerous parameters. It may be possible to improveperformance of one component by optimizing parameters associated withthat component, but because many components share resources or areotherwise coupled, optimizing one component may reduce performance ofanother component. Optimization of an entire computing platform, or of amulti-component subsystem of a platform, may thus require simultaneouslyoptimizing parameters of multiple related components. Real-world systemsmay comprise so many components and configurable parameters, however,that even an optimization method capable of simultaneously optimizingmultiple components might not be able to efficiently or accuratelycomplete the task in a reasonable amount of time.

BRIEF SUMMARY

A first embodiment of the present invention provides a method foroptimizing a cloud-computing environment, the method comprising:

a processor of a computer system receiving extrinsic data that describesa set of virtual resources of the cloud-computing environment;

the processor selecting a set of candidate resource configurations,wherein every virtual resource of the set of virtual resources isassociated with a plurality of candidate resource configurations of theset of candidate resource configurations in a one-to-many relationship;

the processor further selecting a set of candidate environmentconfigurations, wherein each candidate environment configuration of theset of candidate environment configurations comprises at least onecandidate resource configuration of the set of candidate resourceconfigurations;

the processor provisioning a virtual test environment, wherein thevirtual test environment simulates the cloud-computing environment, andwherein the virtual test environment comprises a virtual representationof each virtual resource of the set of virtual resources of thecloud-computing environment;

the processor configuring the virtual test environment by successivelyloading the virtual test environment with combinations of aconfiguration of at least two candidate environment configurations ofthe set of candidate environment configurations and a configuration ofat least two candidate resource configurations of the set of candidateresource configurations, wherein the at least two candidate resourceconfigurations are associated with a same tested resource of the set ofvirtual resources;

the processor evaluating a first resource-fitness characteristic of afirst loaded resource configuration of the at least two candidateresource configurations, wherein the first loaded resource configurationis associated with the same tested resource of the set of virtualresources, and wherein the first resource-fitness characteristic isassigned a value as a function of an operation of the same testedresource within the virtual test environment while the virtual testenvironment is loaded with the first loaded resource configuration;

the processor deriving a value of an environment-fitness characteristicof a first loaded environment configuration of the at least twocandidate environment configurations as a function of an operation ofthe virtual test environment while the virtual test environment isloaded with the first loaded environment configuration;

the processor ranking the at least two candidate resource configurationsas a function of the evaluating and of the deriving;

the processor further ranking the at least two candidate environmentconfigurations as a function of the evaluating and of the deriving;

the processor revising at least one candidate resource configuration ofthe set of candidate resource configurations and at least one candidateenvironment configuration of the set of candidate environmentconfigurations as a function of the ranking and of the further ranking;

the processor repeating the configuring, the evaluating, the deriving,the ranking, the further ranking, and the revising until a terminationcondition is satisfied; and

the processor identifying an optimal environment configuration as afunction of the further ranking.

A second embodiment of the present invention provides a computer programproduct, comprising a computer-readable hardware storage device having acomputer-readable program code stored therein, said program codeconfigured to be executed by a processor of a computer system toimplement a method for optimizing a cloud-computing environment, themethod comprising:

a processor of a computer system receiving extrinsic data that describesa set of virtual resources of the cloud-computing environment;

the processor selecting a set of candidate resource configurations,wherein every virtual resource of the set of virtual resources isassociated with a plurality of candidate resource configurations of theset of candidate resource configurations in a one-to-many relationship;

the processor further selecting a set of candidate environmentconfigurations, wherein each candidate environment configuration of theset of candidate environment configurations comprises at least onecandidate resource configuration of the set of candidate resourceconfigurations;

the processor provisioning a virtual test environment, wherein thevirtual test environment simulates the cloud-computing environment, andwherein the virtual test environment comprises a virtual representationof each virtual resource of the set of virtual resources of thecloud-computing environment;

the processor configuring the virtual test environment by successivelyloading the virtual test environment with combinations of aconfiguration of at least two candidate environment configurations ofthe set of candidate environment configurations and a configuration ofat least two candidate resource configurations of the set of candidateresource configurations, wherein the at least two candidate resourceconfigurations are associated with a same tested resource of the set ofvirtual resources;

the processor evaluating a first resource-fitness characteristic of afirst loaded resource configuration of the at least two candidateresource configurations, wherein the first loaded resource configurationis associated with the same tested resource of the set of virtualresources, and wherein the first resource-fitness characteristic isassigned a value as a function of an operation of the same testedresource within the virtual test environment while the virtual testenvironment is loaded with the first loaded resource configuration;

the processor deriving a value of an environment-fitness characteristicof a first loaded environment configuration of the at least twocandidate environment configurations as a function of an operation ofthe virtual test environment while the virtual test environment isloaded with the first loaded environment configuration;

the processor ranking the at least two candidate resource configurationsas a function of the evaluating and of the deriving;

the processor further ranking the at least two candidate environmentconfigurations as a function of the evaluating and of the deriving;

the processor revising at least one candidate resource configuration ofthe set of candidate resource configurations and at least one candidateenvironment configuration of the set of candidate environmentconfigurations as a function of the ranking and of the further ranking;

the processor repeating the configuring, the evaluating, the deriving,the ranking, the further ranking, and the revising until a terminationcondition is satisfied; and

the processor identifying an optimal environment configuration as afunction of the further ranking.

A third embodiment of the present invention provides a computer systemcomprising a processor, a memory coupled to said processor, and acomputer-readable hardware storage device coupled to said processor,said storage device containing program code configured to be run by saidprocessor via the memory to implement a method for optimizing acloud-computing environment, the method comprising:

a processor of a computer system receiving extrinsic data that describesa set of virtual resources of the cloud-computing environment:

the processor selecting a set of candidate resource configurations,wherein every virtual resource of the set of virtual resources isassociated with a plurality of candidate resource configurations of theset of candidate resource configurations in a one-to-many relationship;

the processor further selecting a set of candidate environmentconfigurations, wherein each candidate environment configuration of theset of candidate environment configurations comprises at least onecandidate resource configuration of the set of candidate resourceconfigurations;

the processor provisioning a virtual test environment, wherein thevirtual test environment simulates the cloud-computing environment, andwherein the virtual test environment comprises a virtual representationof each virtual resource of the set of virtual resources of thecloud-computing environment;

the processor configuring the virtual test environment by successivelyloading the virtual test environment with combinations of aconfiguration of at least two candidate environment configurations ofthe set of candidate environment configurations and a configuration ofat least two candidate resource configurations of the set of candidateresource configurations, wherein the at least two candidate resourceconfigurations are associated with a same tested resource of the set ofvirtual resources;

the processor evaluating a first resource-fitness characteristic of afirst loaded resource configuration of the at least two candidateresource configurations, wherein the first loaded resource configurationis associated with the same tested resource of the set of virtualresources, and wherein the first resource-fitness characteristic isassigned a value as a function of an operation of the same testedresource within the virtual test environment while the virtual testenvironment is loaded with the first loaded resource configuration;

the processor deriving a value of an environment-fitness characteristicof a first loaded environment configuration of the at least twocandidate environment configurations as a function of an operation ofthe virtual test environment while the virtual test environment isloaded with the first loaded environment configuration;

the processor ranking the at least two candidate resource configurationsas a function of the evaluating and of the deriving;

the processor further ranking the at least two candidate environmentconfigurations as a function of the evaluating and of the deriving;

the processor revising at least one candidate resource configuration ofthe set of candidate resource configurations and at least one candidateenvironment configuration of the set of candidate environmentconfigurations as a function of the ranking and of the further ranking;

the processor repeating the configuring, the evaluating, the deriving,the ranking, the further ranking, and the revising until a terminationcondition is satisfied; and

the processor identifying an optimal environment configuration as afunction of the further ranking.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a structure of a computer system and computer program codethat may be used to implement a method for optimizing a cloud-computingenvironment in accordance with embodiments of the present invention.

FIG. 2 is a representation of a computing environment to be optimized inaccordance with embodiments of the present invention.

FIG. 3 illustrates a representation of the computing environment of FIG.2 as a biological ecosystem in accordance with embodiments of thepresent invention.

FIG. 4 illustrates a method for optimizing a cloud-computing environmentin accordance with embodiments of the present invention.

FIG. 5 illustrates details of FIG. 4 that describes a method ofgenerating a set of baseline configurations of components of acloud-computing environment in accordance with embodiments of thepresent invention.

DETAILED DESCRIPTION

A computing platform, such as an enterprise network or a virtualizedcloud-computing environment, may comprise an enormous number ofconfigurable components, and the performance of each component may be afunction of numerous parameters.

Dependencies or other relationships may exist among subsets of theseparameters, making it difficult to optimize performance or efficiency ofsuch a platform by independently optimizing performance or efficiency ofone or more components. In some cases, attempting to optimizeperformance of one component may even reduce performance of anothercomponent when those components share resources or are otherwisecoupled. Optimization of an entire computing platform, or of amulti-component subsystem of a platform, may thus require a method ofconcurrently or iteratively optimizing parameters of multiple relatedcomponents.

Embodiments of the present invention solve this problem by creating amodel of a biological ecosystem that represents, as elements of anatural ecosystem, the computing platform, its components, and theparameters by which each component may be configured. Within thisecosystem model, sets of configurable parameters of a component of thecomputing platform, which may identify all or part of a configuration ofthat component, may be represented as one or more chromosomes of abiological organism.

In such a model, sets of configurable parameters (or chromosomes) may beorganized and combined into a first generation of distinct groups, wherethis organizing and combining is performed as a function ofcharacteristics of the elements' computer-platform analogs, and whereeach group identifies a complete configuration of one component or acomplete genome of an organism. In this biological model, each suchgroup of configurable parameters, if populated with values of theparameters, might be considered analogous to a phenotype of a genome.

Each group of this first generation is then at least partially optimizedby means of optimization methods analogous to natural-selection fitnessfunctions. These optimization methods may include operations that mimican effect of a mutation of a chromosome or of a crossover betweenchromosomes of a same organism, or may comprise other functions that mayhave biological analogs known to those skilled in the art of ecology orin the principles of natural selection, such as regrouping,colonization-extinction, or migration. These optimization methods,performed concurrently in a massively parallel manner upon all groups ofthe first generation, will produce a next generation of groups.

The optimization procedures are then repeated upon this next generationand the process repeats until successive iterations, within resourceconstraints, no longer produce a significant improvement. Thechromosomes of the final generation produced by this method willidentify values of the computing platform's configurable parameters thatrepresent an optimized configuration of the entire platform.

FIG. 1 shows a structure of a computer system and computer program codethat may be used to implement a method for optimizing a cloud-computingenvironment in accordance with embodiments of the present invention.FIG. 1 refers to objects 101-115.

Aspects of the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module,” or “system.”

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In FIG. 1, computer system 101 comprises a processor 103 coupled throughone or more I/O Interfaces 109 to one or more hardware data storagedevices 111 and one or more I/O devices 113 and 115.

Hardware data storage devices 111 may include, but are not limited to,magnetic tape drives, fixed or removable hard disks, optical discs,storage-equipped mobile devices, and solid-state random-access orread-only storage devices. I/O devices may comprise, but are not limitedto: input devices 113, such as keyboards, scanners, handheldtelecommunications devices, touch-sensitive displays, tablets, biometricreaders, joysticks, trackballs, or computer mice; and output devices115, which may comprise, but are not limited to printers, plotters,tablets, mobile telephones, displays, or sound-producing devices. Datastorage devices 111, input devices 113, and output devices 115 may belocated either locally or at remote sites from which they are connectedto I/O Interface 109 through a network interface.

Processor 103 may also be connected to one or more memory devices 105,which may include, but are not limited to, Dynamic RAM (DRAM), StaticRAM (SRAM), Programmable Read-Only Memory (PROM), Field-ProgrammableGate Arrays (FPGA), Secure Digital memory cards, SIM cards, or othertypes of memory devices.

At least one memory device 105 contains stored computer program code107, which is a computer program that comprises computer-executableinstructions. The stored computer program code includes a program thatimplements a method for optimizing a cloud-computing environment inaccordance with embodiments of the present invention, and may implementother embodiments described in this specification, including the methodsillustrated in FIGS. 1-5. The data storage devices 111 may store thecomputer program code 107. Computer program code 107 stored in thestorage devices 111 is configured to be executed by processor 103 viathe memory devices 105. Processor 103 executes the stored computerprogram code 107.

Thus the present invention discloses a process for supporting computerinfrastructure, integrating, hosting, maintaining, and deployingcomputer-readable code into the computer system 101, wherein the code incombination with the computer system 101 is capable of performing amethod for optimizing a cloud-computing environment.

Any of the components of the present invention could be created,integrated, hosted, maintained, deployed, managed, serviced, supported,etc. by a service provider who offers to facilitate a method foroptimizing a cloud-computing environment. Thus the present inventiondiscloses a process for deploying or integrating computinginfrastructure, comprising integrating computer-readable code into thecomputer system 101, wherein the code in combination with the computersystem 101 is capable of performing a method for optimizing acloud-computing environment.

One or more data storage units 111 (or one or more additional memorydevices not shown in FIG. 1) may be used as a computer-readable hardwarestorage device having a computer-readable program embodied thereinand/or having other data stored therein, wherein the computer-readableprogram comprises stored computer program code 107. Generally, acomputer program product (or, alternatively, an article of manufacture)of computer system 101 may comprise said computer-readable hardwarestorage device.

While it is understood that program code 107 for cross-retail marketingbased on analytics of multichannel clickstream data may be deployed bymanually loading the program code 107 directly into client, server, andproxy computers (not shown) by loading the program code 107 into acomputer-readable storage medium (e.g., computer data storage device111), program code 107 may also be automatically or semi-automaticallydeployed into computer system 101 by sending program code 107 to acentral server (e.g., computer system 101) or to a group of centralservers. Program code 107 may then be downloaded into client computers(not shown) that will execute program code 107.

Alternatively, program code 107 may be sent directly to the clientcomputer via e-mail. Program code 107 may then either be detached to adirectory on the client computer or loaded into a directory on theclient computer by an e-mail option that selects a program that detachesprogram code 107 into the directory.

Another alternative is to send program code 107 directly to a directoryon the client computer hard drive. If proxy servers are configured, theprocess selects the proxy server code, determines on which computers toplace the proxy servers' code, transmits the proxy server code, and theninstalls the proxy server code on the proxy computer. Program code 107is then transmitted to the proxy server and stored on the proxy server.

In one embodiment, program code 107 for cross-retail marketing based onanalytics of multichannel clickstream data is integrated into a client,server and network environment by providing for program code 107 tocoexist with software applications (not shown), operating systems (notshown) and network operating systems software (not shown) and theninstalling program code 107 on the clients and servers in theenvironment where program code 107 will function.

The first step of the aforementioned integration of code included inprogram code 107 is to identify any software on the clients and servers,including the network operating system (not shown), where program code107 will be deployed that are required by program code 107 or that workin conjunction with program code 107. This identified software includesthe network operating system, where the network operating systemcomprises software that enhances a basic operating system by addingnetworking features. Next, the software applications and version numbersare identified and compared to a list of software applications andcorrect version numbers that have been tested to work with program code107. A software application that is missing or that does not match acorrect version number is upgraded to the correct version.

A program instruction that passes parameters from program code 107 to asoftware application is checked to ensure that the instruction'sparameter list matches a parameter list required by the program code107. Conversely, a parameter passed by the software application toprogram code 107 is checked to ensure that the parameter matches aparameter required by program code 107. The client and server operatingsystems, including the network operating systems, are identified andcompared to a list of operating systems, version numbers, and networksoftware programs that have been tested to work with program code 107.An operating system, version number, or network software program thatdoes not match an entry of the list of tested operating systems andversion numbers is upgraded to the listed level on the client computersand upgraded to the listed level on the server computers.

After ensuring that the software, where program code 107 is to bedeployed, is at a correct version level that has been tested to workwith program code 107, the integration is completed by installingprogram code 107 on the clients and servers.

Embodiments of the present invention may be implemented as a methodperformed by a processor of a computer system, as a computer programproduct, as a computer system, or as a processor-performed process orservice for supporting computer infrastructure.

FIG. 2 is a representation of a computing environment to be optimized inaccordance with embodiments of the present invention. FIG. 2 compriseselements 210-260.

Reference numeral 210 refers to the computing or communications platformin its entirety. Platform 210 may refer to any sort of computationalenvironment, including but not limited to: a virtualized cloud-computingenvironment, an enterprise network, a cellular network, a wirelesslocal-area network, a server farm, a telecommunications central office,or combinations thereof.

In some embodiments, a computing platform 210 may be part of a largercomputing or communications environment. A virtualized cloud-computingenvironment 210, for example, may be part of a larger distributed mixedphysical/virtual cloud platform.

In examples and figures described herein, computing or communicationsplatform 210 will be generally referred to as a platform 210 or anenvironment 210, and running examples based upon the model of FIG. 2 mayillustrate principles, scope, and functionality of the present inventionby referring to exemplary cloud-computing platforms 210 and componentsof a cloud-computing platform 210. These references are so limitedsolely for illustrative purposes and should not be construed to limitthe scope of embodiments of the present invention to cloud-computingplatforms or environments.

Reference numeral 220 may refer to one or more virtual machinesprovisioned within a cloud-computing platform 210. In embodiments inwhich platform 210 comprises a different type of computing orcommunications environment, 210 might represent an other entity, such asa network backbone segment, a logical partition of a storage device, onecomputing site of a distributed computing environment, or one mainframesystem of a cluster. The distinguishing characteristic is that item 220identifies a high-level structure that comprises one or more instancesof items 230-260.

In embodiments described herein, platform 210 may comprise more than oneentity 220. In our ongoing example, wherein platform 210 is acloud-computing platform, that platform 210 may comprise multiplevirtual machines 220.

Reference numeral 230 identifies one or more resource classesprovisioned within virtual machine 220. If a cloud-computing platform210 comprises multiple virtual machines 220, each such machine 220 maycomprise multiple resource classes 230.

Each resource class 230 identifies one class of computing orcommunications resource instances 220 comprised or hosted by ahigher-level entity or structure 220. Depending on implementationdetails, such a resource may, for example, identify a class of computingsystem, application, system software, or middleware.

In our cloud-computing example, one resource class 230 may identifyApache Tomcat Web servers running within a first virtual machine 220, asecond resource class 230 may identify Oracle WebLogic applicationservers running on the first virtual machine 220, and a third resourceclass 230 may identify an Oracle database-management system running on asecond virtual machine 220. In another example, a first resource class230 may merely identify a class of application servers. Instances ofsuch a resource class would then comprise all instances of both theOracle WebLogic application server and the WebSphere application server.

Reference numeral 240 identifies one or more instances of a resourceclass 230. In the previous example, if three instances of WebLogicapplication servers have been installed and are running on the firstvirtual machine 220, those instances would each be identified as adistinct resource instance 240 of the application-server resource class230, running on the first virtual machine 220.

Reference numeral 250 identifies a defining configuration of an instance240 of a resource class 230. In our cloud-computing example, such adefining configuration might comprise an entire code base (that is, thesource code) of an instance 240 of a WebLogic application-serverresource class 230. There is a one-to-one correspondence between eachitem identified as a defining configuration 250 and each resourceinstance 240. In our cloud-computing example, the three WebLogicinstances 240 of the application-server resource class 230 would eachcomprise one instance of a WebLogic application-server code base 250,and each of these code-base instances 250 would completely definefunctionality of an associated instance 240 of the WebLogic resourceclass 230.

In some embodiments, two distinct instances 240 of an identical resourcemight each comprise an identical but distinct code base or otherdefining configuration 250. In the previous examples, three WebLogicinstances 240 of application-server resource class 230 might eachcomprise an identical instance of the WebLogic application-server codebase 250. But the WebSphere application server instance 240 wouldcomprise a different code base, despite the fact that both the WebLogicinstances 240 and the WebSphere instance 240 belong to the sameapplication-server resource class 230. This distinction may be afunction of the fact that, despite being similar types of applications,the WebLogic application and the WebSphere application comprisedifferent source code.

Reference numeral 260 identifies a functional component of a definingconfiguration 250 of a resource instance 240. In our cloud-computingexample, such a functional component 260 might be a subroutine, module,logical function, or other block of source code comprised by anapplication instance 240, where the functional component 260 performs aspecific function. If resource instance 240 identifies an installed copyof an Oracle database-management system the code base 250 associatedwith that instance 240 of the Oracle DBMS might comprise two hundredfunctional components 260. Here, code base 250 might also comprise otherfunctional components that would be omitted from this representation ifthose omitted components are not relevant to the optimization taskperformed by the embodiment.

In this example, a component 260 of the two hundred functionalcomponents might perform a function like parsing a user query,traversing a database index, updating a field, or performing logicaloperations comprised by a query string. Each of these functionalcomponents would be associated with one or more configurable parameters.Such parameters might include, but are not limited to, combinations offactors such as: a maximum number of query terms, a maximum number ofqueries that may be parsed concurrently, a maximum allowable number ofthreads per process, a maximum amount of memory that may be allocated toa process, or a limit on the size of a stored record.

In our application-server example, a code base 250 of an instance 240 ofa WebLogic application server might comprise functional modules 260 thateach performs a function like maintaining a client connection ormanaging server processes. Such functional modules or components 260might be associated with configurable parameters like a KeepAliveTimeoutparameter (an amount of time a server is allowed to wait for userrequests received through a persistent connection), a MaxSpareServersparameter (a maximum allowable number of idle processes),MinSpareServers (a minimum allowable number of idle processes), or aMaxClients parameter (a maximum allowable number of simultaneousconnections).

A goal of embodiments of the present invention is to identify optimal,or most nearly optimized, values of these configurable parameters, eachof which is associated with a functional component 260 of a code base250 of a resource instance 240 of a resource class 230, whereconfiguring each resource instance 240 with the optimal values optimizesoverall performance, efficiency, or an other desirable characteristic ofcomputing platform 210.

A goal of some embodiments of the present invention is to identify anoptimal, or most nearly optimized, configuration of an entire platform210. In some cases, an optimal configuration of a platform 210 may notcomprise optimal configurations of every resource instance 240, or ofevery functional block 260 of a resource instance, comprised by theplatform 210.

In such cases, the iterative process of the present invention may stillperform optimization procedures on every optimizable component of aplatform 210 and on the platform 210 itself, and will thus generallyincrease a degree of optimization of each logical group of configurableparameters. But it is possible that resource constraints, userrequirements, dependencies among resource instances, and other factorsmay prevent some groups of parameters from converging to an optimalstate, and it is possible that an optimal, or most nearly optimized,configuration of the platform 210 may not comprise optimal, or mostnearly optimized, configurations of one or more components of theplatform 210. In such cases, an embodiment of the present invention may,by means of the massively parallel iterative procedure of FIG. 4,optimize both a platform 210 and its components concurrently in order tocorrectly identify an optimized, or most nearly optimized configurationof platform 210, regardless of whether this configuration comprisesoptimized, or most nearly optimized configurations of every component ofthe platform 210.

FIG. 3 illustrates a representation of the computing environment of FIG.2 as a biological ecosystem in accordance with embodiments of thepresent invention. FIG. 3 comprises elements 310-360.

One feature of the present invention is its method of organizing andstructuring characteristics of a computing or communications platform inways that are similar to ways in which certain characteristicsassociated with a biological ecosystem may be organized and structured.This similarity may be seen clearly by comparing each element of FIG. 3to its analogous element in FIG. 2.

Reference numeral 310 identifies an ecosystem that, in accordance withembodiments of the present invention, may be analogous to computing orcommunications platform 210 of FIG. 2. For purposes of this metaphor,platform 310 may refer to any sort of biological ecosystem, including,but not limited to, a rainforest, a stream or lake, a deep-sea region anocean, an insect colony, digestive tract of an animal, a subset of anyof these examples, or combinations hereof.

Exemplary embodiments of the present invention described here mapelements of one computing or communications environment onto elements ofone ecosystem. But this should not be construed to limit the presentinvention to a single-ecosystem model. A virtualized cloud-computingenvironment 310, for example, may be part of a larger distributed mixedphysical/virtual cloud platform. Ecosystem 310 may be part of, or maycomprise, one or more other ecosystems 310 or other biologicalenvironments, and multiple ecosystems 310 may be adjacent or nested inany combination. For the sake of simplicity, the examples of FIGS. 2 and3 describe a single ecosystem 310 that may be used to model a singlecloud-computing platform 210.

Reference numeral 320 refers to one or more habitats comprised byecosystem 310. Here, in accordance with embodiments of the presentinvention, such a habitat 320 may be analogous to a high-level structureor virtual machine 220 of FIG. 2.

Each habitat 320 describes a subset of ecosystem 310 that is inhabitedby one or more species 330. Many examples exist of such habitats,including, but not limited to: a valley 320 within a temperate forestedecosystem 310; a coral reef 320 of an ocean shoreline 310; or a secludedslope 320 of a mountain range 310.

Reference numeral 330 identifies one or more species of organism thatinhabits habitat 320. Here, in accordance with embodiments of thepresent invention, such a species 330 may be analogous to a resourceclass 230 of FIG. 2.

If an ecosystem 310 comprises multiple habitats 320, each habitat 320may comprise multiple species, each of which is identified as a distinctspecies 330. Each of these species 330 identifies one distinct type oforganism 340 that inhabits habitat 320. As is known to those skilled inthe art, an enormous number of species of organisms exist in nature. Afew isolated examples of species (including some that biologists mightclassify as a subspecies) include: Siberian tigers, Japanese sea lions,bald eagles, and Western black rhinoceros.

Each instance of reference numeral 340 identifies an organism of aspecies 330. In accordance with embodiments of the present invention,such an organism 340 may be analogous to a resource instance 240 of FIG.2.

In one example, if a community of 30 Siberian tigers live on a mountainslope habitat 320 of an ecosystem 310 that encompasses the northernportion of the Eastern Manchurian mountain range, each member of thatcommunity would be identified in FIG. 3 as an instance of a distinctorganism 340 of the Siberian tiger species 330.

Reference numeral 350 identifies a genome of an organism 340. Inaccordance with embodiments of the present invention, a genome 350 maybe analogous to a code base or other defining configuration 250 of aresource instance, as shown in FIG. 2. There is a one-to-onerelationship between each genome 340 and each organism 350; that is, thecharacteristics of each organism 340 are defined by one genome 350.

In some embodiments, two identical but distinct organisms 340 of aspecies 330 might each comprise an identical but distinct instance of agenome 350. As will be described below, each genome 350 comprises aplurality of chromosomes 360.

Reference numeral 360 identifies a chromosome comprised by a genome 350of an organism 340. In accordance with embodiments of the presentinvention, a chromosome 360 may be analogous to a functional component260 of a code base 250. A genome 350 may comprise a large number ofchromosomes 360, each of which may be associated with one or morecharacteristics of the organism 340. In some embodiments, all organisms340 of a same species 330 comprise a genome 350 that contains a same setof chromosomes 360.

Some chromosomes 360 may not be associated with a characteristic of anorganism 340 that is related to the organism's fitness, just as somefunctional blocks 260 may not be associated with functions or parametersof an associated resource instance 240 that are related to the resourceinstance's performance, efficiency, or other optimizable characteristic.

FIG. 4 illustrates a method for optimizing a cloud-computing environmentin accordance with embodiments of the present invention. FIG. 4comprises steps 400-480.

In step 400, one or more processors of a computer system receiveextrinsic data about a cloud-computing environment 210, or an othercomputing or communications platform 210, that is to be optimized. Thisextrinsic data allows the one or more processors to organize elements ofthe environment or platform 210 to be optimized, wherein the organizedelements are structured into a framework similar to that shown in FIG.2.

In addition to descriptions of the organization of the platform orenvironment 210 and of characteristics of each component of interestcomprised by the platform or environment 210, the extrinsic data mayalso comprise sets of user requirements that identify desiredperformance, resource-consumption, or efficiency standards, or thatidentify parameters by which a degree of optimization of platform 210may be identified.

The extrinsic data may also comprise historical data that identifiesperformance characteristics and other characteristics of the environment210 under real-world loads. The extrinsic data may also comprise adescription of each element of the environment 210, includingdescriptions of each virtual machine or other computing system 220, ofeach resource instance 240, of the resource class 230 of each instance240, and of the code base 250 and functional blocks 260 comprised byeach resource instance 240.

In one example, the extrinsic data may identify a cloud-computingplatform 210 that comprises 140 virtual machines 220. It may furtheridentify or characterize one or more resources 240 provisioned on eachvirtual machine 220, such as specifying that seven WebLogic applicationservers (server instances 240) are running a first virtual machine 220of the 140 virtual machines, and that each of the seven installedWebLogic instances 240 belong to an application-server resource class230.

The extrinsic data may further identify or characterize each element ofthe environment or platform 210 down to the level of configurableparameters comprised by each functional block 260. In our precedingexample, this further identifying or characterizing might compriseidentifying a code base 250 (a complete set of source-code instructions)associated with a WebLogic application server and would associate aninstance of this WebLogic code base 250 with each installed instance ofthe WebLogic application server 240.

In this example, the extrinsic data might also identify four hundredfunctional blocks 260 comprised by each WebLogic code base 250associated with one of the installed WebLogic instances 240. Each ofthese functional blocks 260 might then be associated with a set ofconfigurable parameters.

In some embodiments, the extrinsic data may identify characteristics ofeach configurable parameter. These characteristics might comprise, butare not limited to, a range of acceptable values a parameter may assume,a smallest value by which such a value may be incremented. The extrinsicdata may, for example, identify a first configurable parameterMaxKeepAliveRequests comprised by a first communications-servicesfunctional block 260 of a WebLogic code base 250 of a first instance 240of a class 230 of WebLogic application servers. In such a case, theextrinsic data might further identify and associate withMaxKeepAliveRequests a max value of 100, a min value of 50, and a delta(incremental) value of 10. This would allow the processors to identifythat MaxKeepAliveRequests may assume any value in the set {50, 60, 70,80, 90, 100}. In related embodiments, such extrinsic information mightbe further interpreted as permitting the one or more processors toadjust an existing value of the MaxKeepAliveRequests parameter byincrementing or decrementing a current value by the delta value, so longas the resulting value remained within the max-min range.

The extrinsic data may also comprise historical or archived trafficdata, or information culled from a social network or from otherextrinsic source, that identifies past demands on elements of theplatform 210 and past responses of the platform 210 to such demands.This data may include, but is not limited to, combinations of historicalinformation associated with numbers of user requests, amounts ofrequested bandwidth, memory usage, fluctuations in available storagecapacity, or response time.

The extrinsic data may also comprise information about the values of oneor more configurable parameters at a time at which an element of thehistorical data was recorded. The data might, for example, identifythat: during the final week of the first quarter of 2014, the number ofincoming requests to connect to a first WebLogic instance 240 installedon a fourth virtual machine 220 fluctuated between 75 requests per hourand 1200 requests per hour; the screen-refresh response time of theWebLogic instance 240 fluctuated during that period, as a function ofthe number of requests received per hour, within a range of 0.06 secondsand 1.1 seconds; and that the range of the fluctuations varied inverselyin proportion to a configurable memory-allocation parameter of ascreen-display function 260 of the first WebLogic instance 240.

In one example, the extrinsic data may identify past fluctuations inbandwidth requests made by viewers of a sporting event when a starplayer is introduced. This data may help system designers identify amore realistic range of projected bandwidth requirements the next time astar player appears.

Other extrinsic information may be received in step 400 that may allowthe one or more processors to identify a more detailed or more accurateframework. The extrinsic information might, for example, identifydependencies or relationships among resource instances, functionalblocks, or configurable parameters. In the previous example, theextrinsic data might identify a dependency relationship between actualvalues of the configurable memory-allocation parameter and concurrentactual values of the screen-refresh response time of the WebLogicinstance, or might identify a more complex relationship between valuesof both the memory-allocation parameter and the screen-refresh responsetime and the rate of connection requests identified by historicaltraffic data. In some embodiments, the extrinsic data may also includeuser requirements that identify characteristics of the method of FIG. 4,such as, but not limited to, a maximum number of generations to begenerated during the iterative optimization process of steps 420-480 amaximum or minimum number of individuals comprised by each generation,or a method of weighting an importance of a first parameter relative tothat of a second parameter.

The extrinsic data received in step 400 may be derived from an entitythat owns, manages, or otherwise has expert or historic knowledge ofcharacteristics of the platform 210. Such an entity might include, butis not limited to, one or more of: a system administrator, aninformation technology or maintenance technician, an automated ormanually compiled system log, a statistical analysis, members of a userbase, or a historical record.

Once the one or more processors have organized elements of the platform210 into a framework of the form illustrated in FIG. 2, the one or moreprocessors may then generate an analogous biological framework of theform illustrated in FIG. 3, wherein each element of the platformframework of FIG. 2 is mapped onto a corresponding element of thebiological framework of FIG. 3. This mapping correspondence is describedin detail in the descriptions above of FIG. 2 and FIG. 3.

In the preceding example, such a mapping might result in correspondencesthat comprise: associating each installed instance 240 of the WebLogicapplication server onto an analogous “WebLogic” organism 340;associating the application-server resource class 230 onto an“application-server” species 330; associating the screen-displayfunction 260 one a first WebLogic instance 240 onto a “screen-display”chromosome 360 of a first “WebLogic” organism 340; and associating thememory-allocation parameter of the screen-display function 260 onto a“memory-allocation” gene comprised by the “screen-display” chromosome360.

It is important to remember that, despite the biological metaphor usedthroughout this document, the present invention comprises acomputer-implemented system and method, and is in the field of computerscience, not directly related to any of the biological or naturalsciences. The biological metaphor is used here as a conceptual frameworkthat allows correspondences to be drawn between characteristics ofparameters of an optimizable non-biological system and characteristicsof a self-optimizing biological system. The present invention thenperforms computerized steps that exploit these correspondences byperforming operations that have effect similar to that of certainelements of a natural-selection process.

Such a procedure may be applied in analogous manner to other types ofoptimizable systems not described in detail in this document, so long asthose systems may be decomposed into a framework that may then bemodeled as a biological or ecological system, and so long as thosesystems may be optimized by means of optimizable parameters that may bemodeled as genes or chromosomes of the biological or ecological system.

Examples of such an optimizable system comprise, but are not limited to:a product-distribution channel; a warehouse inventory; a mobilecongregation of individuals, such as a national sales force, an armydivision, or a touring stage show; and a management or engineeringproject. Such systems all have in common two features: i) a set ofcomponent entities that may be characterized or configured by values ofconfigurable parameters, where a first set of these values may allow acomponent, or the system as a whole, to perform its function in ahigher-performing, more efficient, or otherwise more desirable mannerthan would a second set of values; and ii) a hierarchical internalstructure of a form that allows the system to be modeled as a biologicalor ecological ecosystem.

At the conclusion of step 400, the one or more processors will havegathered all information necessary to generate an initial configurationof the models of FIG. 2 and FIG. 3 and will have used this informationto organize components of a computing or communications platform (suchas a cloud-computing environment 210) into a pair of analogous models,as explained above in the descriptions of FIG. 2 and FIG. 3, where thetwo generated models respectively represent components of theenvironment/platform 210 within a non-biological systems context and anecological context.

At the conclusion of step 400, the one or more processors may also haveidentified a set of fitness functions that may be used to measure adegree of optimization of a functional block 260/chromosome 360, of aplatform 210/ecosystem 310 as a whole, or of a parameter/gene.

A fitness function may take any form by which an entity's “fitness,” ordegree of optimization may be quantized, and may perform suchquantizations as functions of multiple parameters or other factorsassociated with a performance of a platform or environment 210.

A fitness function of a response-time parameter, for example, may returna value that is inversely proportional to a value of the response-timeparameter within a threshold range. If a user requirement received instep 400 identifies that a “perfect” response time is 1.0 second andthat decreasing the response time below 1.0 second produces negligibleimprovement, than a fitness function associated with the response-timeparameter might return a value that is an inverse value of responsetimes greater than 1.0 second, and returns a value of 1.0 for anyresponse time less than 1.0 second. In this case, a higher returnedfitness value would indicate a more closely optimized response time.

Fitness functions may take any form that is deemed to identify a degreeof optimization of an optimizable entity. In some cases, a fitnessfunction may be complex, such as a fitness function that identifies adegree of optimization of an entire cloud-computing platform 210. Suchfitness functions may be derived through means known in the art, or as afunction of expert knowledge of the structure and operation of theplatform 210. In one example, a platform-encompassing fitness functionmight return a value that indicates an overall fitness or degree ofoptimization of a platform 210/ecosystem 310 by evaluating a polynomialfunction in which each term of the polynomial is a function of a fitnessvalue of a configurable parameter/gene comprised by the platform210/ecosystem 310. In such a case, the polynomial might be simplified bynulling out values related to any parameter that is not relevant to theoptimization procedure. In one example, if a configurable parameter thatdetermines a color scheme of a Web server 240's graphical user interfacehas no relationship to an efficiency or performance of any function ofthe Web server 240, values of that color-scheme parameter would not beincluded in any fitness functions derived in this step.

Embodiments of the present invention may comprise two classes of fitnessfunction. A resource (or organism) fitness function identifies a fitnessof an instance 240240 of a resource class 230. Fitness may be a functionof factors that comprise, but are not limited to, performance orefficiency measurements, such as a response time, an average memoryusage, or an average storage usage, and each factor may be weighted torepresent relative importance or relevance of each factor.

An environment (or ecosystem) fitness function may identify or quantizesimilar fitness parameters of an entire platform 210 and may compriseresource fitness functions associated with one or more resourceinstances 240 comprised by the platform 210. User may determine thatresponse time is 70% weighted importance, storage utilization 20%, etc.

Embodiments of the present invention may evaluate “N-evolutionary”fitness by using both types of fitness functions to concurrentlyevaluate fitness of a platform 210 and of N resource instances 240comprised by the platform 210. This N-evolutionary characteristic allowsthe massively parallel, iterative procedure of the present invention toidentify relative degrees of fitness (or degrees of optimization) of acomplex platform 210 that comprises resources that are coupled bydependencies and other relationships.

In some embodiments, part of this modeling procedure may be delayeduntil a step of the procedure of FIG. 5 is performed. This may occur,for instance, if it is not possible to identify and characterize some orall relevant optimizable components or parameters during step 400because the information received in step 400 is incomplete.

In step 410, the one or more processors use the extrinsic informationreceived in step 400 to generate a first generation of baselineconfigurations of “chromosomes” 360. As described above and below, eachsuch chromosome 360 represents a set of configurable parametersassociated with a functional block of an organism 340/resource instance240.

The method by which this first generation of configurations is assignedinitial values is described in greater detail in steps 500-550 of FIG.5.

These baseline configurations each comprise a data structure thatrepresents one or more chromosomes 360. Each of these representations isstructured as a function of characteristics of one or more functionalblocks 260 that correspond to the one or more chromosomes. Each of theserepresentations is then populated as a function of elements of theextrinsic data received in step 400.

In one example, consider a first functional block 260 of a WebLogicinstance 240 running on a cloud platform 210. Here, the first functionalblock 260 might represent source code that performs a function ofmanaging users' connections to the WebLogic instance 240. In thisexample, if the first functional block 260 is associated with twelveconfigurable parameters (or “genes”), eight of which are relevant to atask of optimizing platform 210 (or of optimizing a component ofplatform 210), a baseline configuration of the corresponding chromosome360 might be represented as an 8-element data structure, where eachelement of the data structure (or, within our biological metaphor, each“gene of the chromosome”) identifies a value of one of the eightconfigurable parameters. As explained in the description of FIG. 5, eachof these values may be determined as a function of the extrinsic datareceived in step 400.

In some embodiments, step 410 and the method of FIG. 5 might group allbaseline configurations associated with one resource instance240/organism 340 into a single corresponding group of chromosomes 360.In such embodiments, at the conclusion of step 410, the one or moreprocessors may have constructed a set of “chromosome group” datastructures that may each have been populated with values ofcorresponding parameters/genes, and that each identifies one possibleconfiguration of one resource instance 240/organism 340.

In some embodiments, the one or more processors may, at the completionof step 410, have generated multiple instances of a chromosome datastructure or of a chromosome group data structure, wherein each instanceof the multiple instances comprises an identical data structure, but ispopulated with a different set of values. In such a case, each suchinstance corresponds to a candidate configuration of a correspondingfunctional block or blocks 260. Subsequent steps of the method of FIG. 4would then evaluate these multiple instances and attempt to select thefittest, or most nearly optimized, instance.

Step 420 initiates an iterative process of steps 420-480, where eachiteration of this iterative process evaluates the current generation ofconfigurations and determines whether that generation has been optimizedto an extent that satisfies user requirements received in step 400. Ifthe current generation is sufficiently optimized, or if an otherpreviously defined termination condition has occurred, then theiterative process ends and the current generation is deemed to beoptimal, within user and resource constraints. If the current generationis not found to be sufficiently optimized and if no other previouslydefined termination condition has occurred, then a next generation ofconfigurations is generated from the current generation and theiterative process begins a next iteration in order to evaluate that nextgeneration.

In some embodiments, the predefined termination condition may cause theiterative process to terminate if a certain number of iterations havebeen performed, if a certain duration of time has elapsed, or if themethod of the present invention has consumed a certain amount of aspecified resource. In other embodiments, the iterative process willcontinue until differences between successive generations decrease to anextent that the generations will be deemed to have converged to anoptimal state. In some embodiments, the iterative process will continueuntil some combination of these methods, or some function of acombination of these methods, results in the identification of atermination condition.

In step 430, the one or more processors insert each configuration of thecurrent generation into a test environment. This test environment may beidentified and configured as a function of extrinsic informationreceived in step 400, and this identification and configuration may beperformed by means of expert knowledge of characteristics of theplatform 210 and of one or more entities comprised by the platform 210,of a goal of the embodiment, of an implementation-related constraint, ofa user requirement received in step 400, of historical data received instep 400, or of any other implementation-dependent factor that may helpidentify a real-world condition to which an entity to be optimized maybe subject.

In one example, if the platform or environment 210 to be optimized is acloud-computing environment, the test environment might comprise avirtualized test environment in which have been provisioned resourcesanalogous to resources provisioned of the real-world platform orenvironment 210. Similarly, if a real-world cloud platform 210 to beoptimized comprises seven instances of a WebLogic application server,the test environment would comprise seven similar instances. And if userrequirements require the cloud platform 210 to be optimized to handle aspike in user bandwidth requests when a start athlete comes to batduring a sporting event that will be streamed by means of the WebLogicinstances, the test environment would be configured to simulate asimilar spike.

In some embodiments this test environment may be an “offline” evaluationenvironment, in which external demands, resource constraints, and otherextrinsic conditions of the test environment are simulated. Thesesimulated values may be derived by any means known to those skilled inthe art of software simulation or those who possess expert knowledge ofa characteristic of the real-world platform 210 to be simulated by testenvironment. Such means may comprise selecting simulated values as afunction of elements of the extrinsic data received in step 400, or maycomprise values selected by statistical means or as a function of valuescomprised by other platforms that share characteristics with thereal-world platform 210 to be simulated.

In some embodiments the test environment may be an “online” evaluationenvironment, in which external demands, resource constraints, and otherextrinsic conditions of the test environment are similar to those of thereal-world platform 210 being simulated. Such an online evaluationenvironment may be implemented by means known to those skilled in theart of test engineering. In one example, wherein a real-world platform210 comprises a cloud-computing environment, a test environment may becreated by forking real-world data associated with the real-worldplatform 210 to a substantially identical, but distinct, test platform,such that both the real-world platform 210 and the test experiencesubstantially identical operating conditions during the time in whichthe current generation of configurations is evaluated. In otherembodiments, wherein a system to be optimized is not a computer system,a test environment may be configured by analogous,implementation-dependent, methods known to those skilled in the art ofthe field of the embodiment.

In step 440, each baseline configuration of the current generation ofconfigurations is evaluated or “stress-tested.” This evaluation maycomprise allowing the test environment to operate under constraints andloads identified in step 430, or as a function of information receivedin step 400. During such an evaluation, configurable parameters of oneor more components of the test environment may assume values selected asa function of values of configurable parameters (or “genes”) of one testconfiguration.

In some embodiments, a distinct set of candidate configurations mayexist for every resource instance to be evaluated, and a complete suiteof evaluation procedures might comprise successively evaluating some orall configurations associated with combinations or permutations of theeach resource instance being evaluated. In some embodiments, theseevaluations of components of a platform 210 may be performed in furthercombination with candidate configurations of the platform in itsentirety. Although this feature might result in the one or moreprocessors performing many thousands of evaluations during eachiteration of step 440, such a massively parallel evaluation procedureallows embodiments of the present invention to concurrently optimizecomplex environments that comprise numerous components, and that may besubject to resource constraints, fluctuating demands or loads, andcomplex dependency relationships among comprised components.

In one example, if a current generation of candidate configurations tobe evaluated comprises ten configurations of an instance of an Oracledatabase application 240, ten sets of evaluations may be performed, oneset for each candidate configuration. Here, each candidate configurationis distinguished by its distinct set of values of the configurableparameters associated with the Oracle application, and evaluating such acandidate Oracle configuration would entail configuring the Oracleinstance provisioned within the test environment with parametric valuesidentified by the candidate configuration under test.

Each of these ten evaluations might further test a combination of one ofthe ten Oracle test configurations with a test configuration of anotherresource instance, or of a test configuration of the platform 210 in itsentirety. If, for example, a goal of the evaluation is to identify anoptimized configuration of a subsystem that comprises the Oracleapplication 240 and a WebSphere application 240, and if the currentgeneration of WebSphere configurations comprises 25 candidateconfigurations, testing all possible combination configurations of thetwo applications would require 250 evaluations, each of which loads thetest environment with parametric values of one of the ten Oracleconfigurations and with parametric values of one of the 25 WebSphereconfigurations.

As described above, each of the Oracle candidate configurationscomprises a data structure that identifies a distinct set of values ofeach configurable parameter associated with the Oracle application 240.As explained in the description of step 410, this data structure mightcomprise a group of all chromosome 360 data structures associated withthe Oracle resource instance 240. In this example, step 410 may, forexample, have identified configurable parameters (or “genes”) comprisedby each functional block 260/chromosome 360 associated with the Oracleapplication 240/organism 340, and then organized each chromosome'sparameters into a data structure associated with that chromosome. Theone or more processors would have then grouped these Oracle-associatedchromosome data structures into a larger Oracle “group” chromosome datastructure that comprises all configurable parameters of all functionalblocks 260 associated with the Oracle application 240.

By methods of step 410 and FIG. 5, the one or more processors would havespawned a generation of candidate configurations that conform to thisgroup data structure. Each configuration of this generation would thenbe populated with a distinct set of values of the configurableparameters associated with the Oracle application 240. In the firstgeneration, created in step 410, these values may be selected as afunction of elements of the extrinsic data received in step 410 or bymeans of a method of FIG. 5. Subsequent generations may be populated byvalues selected by procedures of steps 450 and 460, which performoperations upon the fittest (or most nearly optimized) candidates of acurrent generation in order to generate a next generation.

In step 450, the one or more processors select and rank the candidateconfigurations evaluated in step 440. This selecting and ranking isperformed by means of one or more fitness functions configured by meansknown to those skilled in the art as a function of information receivedin step 400.

In some embodiments, a distinct fitness function may be configured foreach resource instance 240/organism 340. Within the scope of thepedagogical biological model of FIG. 3, a fitness function may produce aresult similar to that produced by one component of a biologicalnatural-selection process. If, for example, ten candidate configurationsof an Oracle application 240 are each successively evaluated in step440, a fitness function configured to characteristics of the Oracleapplication would analyze results of each of the ten evaluations andwould score or rank each candidate configuration as a function of theresults.

Any component of a platform 210/ecology 310 may be associated with oneor more fitness functions and, in some embodiments, a single fitnessfunction or set of fitness functions may be associated with acombination of components of the platform 210. In some embodiments, oneor more platform fitness functions may be associated with a platform 210in its entirety, where the one or more platform fitness functionsidentify, score, or rank the fitness of a platform configuration thatmay comprise a combination of candidate configurations that are eachassociated with one or more components of the platform 210.

Within the scope of our systems and biological models of FIG. 2 and FIG.3, a fitness of an entity is analogous to a degree of optimization. If,for example, a first platform configuration of a cloud-computingplatform 210 is identified by a platform fitness function as being morefit than a second platform configuration of the platform 210, the valuesof the parameters comprised by that first platform configuration may bedeemed more fully optimized than the parametric values comprised by thesecond platform configuration. Using a fitness function to rankcandidate configurations is thus analogous to determining which set ofparametric values is closest to optimal.

In a simple example, ten candidate configurations of a WebLogicapplication server 240 may each comprise a distinct set of values ofconfigurable parameters associated with the WebLogic application. Suchparameters might include: a disconnect time that identifies how long auser's connection is sustained while there is no user input; an upperlimit to an amount of storage space allotted to each user; an upperlimit to an amount of network bandwidth allotted to each user; and anumber of transactions that a user may perform concurrently. These tenconfigurations may each be loaded into and evaluated within a testenvironment similar to a real-world cloud-computing environment 210 tobe optimized.

During each candidate configuration's evaluation, the test environmentis configured with estimated real-world loads and resource constraintsthat may help identify how well the cloud-computing environment 210 tobe optimized is able to satisfy user requirements when the WebLogicserver 240 is configured with values comprised by the candidateconfiguration under test. In our example, the estimated real-world loadsand constraints might limit the environment 210's overall system I/Obandwidth to 100 Mbps, and overall available storage capacity to 950terabytes.

In this example, a simple fitness function associated with the WebLogicapplication server instance 240 might in step 450 identify or rank thefitness or degree of optimization of each candidate configuration as afunction of the WebLogic server 240's overall response time under theconditions described above. Other fitness functions may be more complex,and may involve functions of multiple types of measurable results. Insome embodiments, a fitness function may be a polynomial in which eachterm of the polynomial is associated with one parameter or set ofparameters, or in which each coefficient of a term represents a relativeweighting of that term in the fitness evaluation.

In the ongoing simple example, in which a WebLogic fitness functionidentifies a fitness or degree of optimization of a candidateconfiguration of the WebLogic server 240 solely by the WebLogicapplication's overall average response time, the one or more processorsin step 440 evaluate each candidate configuration by configuring theproperly loaded and constrained test environment with parametric valuesidentified by the candidate configuration, and then forward to theWebLogic fitness function the resulting value of the WebLogicapplication's overall average response under these test conditions. Atthe conclusion of the evaluation of ten WebLogic server 240 candidateconfigurations, the WebLogic fitness function will have performed afitness operation on each of the ten resulting average response times.These ten fitness operations will produce final values or scores thatallow the ten configurations to be ranked in terms of fitness or degreeof optimization.

At the conclusion of step 450, one or more fitness functions will haveranked the candidate configurations evaluated in step 440. Based on userrequirements received in step 400, or on a designer's expert knowledgeof platform 210, the one or more processors will have selected two ormore fittest, or most nearly optimized, candidate configurations of eachset of candidate configurations as a foundation for the next generationof candidate configurations.

In one example, a platform 210 comprises 75 resource instances, each ofwhich is associated with a group of functional blocks 260/chromosomes360 that each identifies a set of configurable parameters associatedwith one of the resource instances. If each generation comprises 100candidate configurations associated with each of the 75 resourceinstances, and if the embodiment's fitness functions are configured toidentify and rank the ten fittest candidates for each resource instance,then at the conclusion of step 450, the one or more processors will haveselected from a current generation of 7500 candidate configurations the750 most nearly optimized configurations—ten for each of the 75 resourceinstances—and will in the next step generate a next generation ofcandidate configurations from these 750 selected configurations.

In step 460, the one or more processors generate a next generation ofcandidate configurations from the sets of fittest, or most nearlyoptimized, configurations identified in step 450.

This generating the next generation may comprise performing operationsupon the fittest configurations selected in step 450, where thoseoperations may be considered to be analogous to a crossover function ofa natural-selection process. This crossover function may be implementedby any means known to those skilled in the art of data analysis orcomputer science, and parameters that guide the operation of thecrossover function may be determined by means known to those skilled inthe art as a function of implementation-dependent details or of expertknowledge of user requirements.

In biological terms, a crossover event generates a child chromosome bypartially combining genetic material of two parent chromosomes. In thesystems model of FIG. 2, this operation may be implemented by analogousoperations performed upon two or more functional blocks 260/chromosomes360.

If, for example, a first parent instance of a WebLogic chromosome datastructure comprises four elements of data (“genes”) that each identify avalue of a configurable parameter of a WebLogic resource class 230:

2200 14 00 701102

and a second parent instance comprises:

3010 7 00 316640

then performing a crossover operation on the two parents might producetwo child WebLogic chromosomes formed by swapping the chromosomes'fourth gene:

3010 7 00 701102 2200 14 00 316640

Crossover functions may vary as a function of implementation-dependentdetails. Crossover functions may, for example, vary in the way that theyselect which gene or genes of a selected chromosome to swap; aproportion of the total number of chromosomes in a genome or in achromosome group to cross over; or a number of child chromosomes togenerate by means of a crossover function for each generation ofcandidate configurations.

More sophisticated crossover functions may implement this operation inmore complex or nuanced ways. A variant of a standardtournament-selection procedure, for example, might comprise a selectionstrategy of randomly selecting a predetermined portion of the populationof fittest configurations identified in step 450, crossing the twofittest configurations of the random sample to produce a childconfiguration, adding the child to the population of fittestconfigurations, choosing a new random selection from the updatedpopulation, and repeating until the population has grown to a desiredsize.

In some embodiments, a goal of step 460 will be to add enough new childconfigurations to the selections chosen in step 450 to ensure that thenext generation of candidate configurations will comprise the samenumber of individuals as did each of the previous generations.

In some embodiments, the one or more processors may in step 460 mutatethe populations of fittest configurations selected in step 450 andsubsequently increased in step 460 through crossover operations.

In nature, a mutation of a chromosome comprises a random variation ofone or more genes of the chromosome. In our model, a mutation functionmay perform an analogous operation by randomly varying one or morevalues of a functional block 260.

As with crossover operations, characteristics of a mutation function mayvary as a function of implementation-dependent details. A mutationfunction may, for example, be fine-tuned by adjusting a chance ofmutation. In such a case, an embodiment that specifies a 1% chance ofmutation may apply a mutation operation to one gene out of every hundredgenes, to one chromosome out of every hundred chromosomes, or to onecandidate configuration out of every hundred candidate configurationsassociated with a particular resource instance 240/organism 340.

Mutating a functional block 260/chromosome 360 of a resource instance240/organism 340 generally comprises randomly varying one or moreparametric values associated with the resource instance 240 and with thefunctional block 260. In embodiments in which the extrinsic informationreceived in step 400 comprises a range and minimum increment of valuesof the parameter undergoing mutation, the value of the parameter aftermutation may be selected by any means known to those skilled in the art,so long as the value conforms to the specified range and minimumincrement. A value of a mutation may, for example, be selected randomlyfrom values that fall within the specified range, may be selected as afunction of a statistical distribution, may be selected as a function ofhistorical values, or as a function of multiple criteria.

In some embodiments, the one or more processors may, in step 460,perform crossover and mutation operations in any order, or may omit oneor both of these operations as required by implementation-dependentdetails or user requirements. In some embodiments, a mutatedconfiguration may replace its parent configuration in the nextgeneration of configurations. In other embodiments, the next generationwill comprise both the mutated configuration and the parentconfiguration from which it was generated.

At the conclusion of step 460, the one or more processors will havegenerated a next generation of candidate configurations, where that nextgeneration comprises the fittest (most nearly optimized) candidateconfigurations of the most recent generation, where the next generationmay further comprise new configurations generated by crossing otherconfigurations of the most recent generation, and where one or more ofthe configurations of the next generation have been created by amutation of an other configuration.

In step 470, the one or more processors determine whether a terminationcondition has been satisfied. If so, a termination flag is set to TRUEin step 480, the method of FIG. 4 terminates and the fittest platformconfiguration, as selected in steps 450 or 460, is identified as a mostnearly optimized configuration. In some embodiments, this determinationmay occur prior to any mutation or crossover operations of step 460.

If, the one or more processors determine that a termination conditionhas not been satisfied, then the current iteration of the iterativeprocess of steps 420-480 ends and the next iteration begins. This nextiteration will process the next generation of candidate configurations,as generated in the most recent iteration of steps 450 and 460.

Selection of a termination condition may be implementation dependent andmay be a function of resource constraints, other technical constraints,financial constraints, timing or scheduling constraints, or otherfactors identified by system designers.

Some embodiments may, for example, specify a termination conditions thatis satisfied when the one or more processors identify a configurationthat, even if not optimal, satisfies a set of minimum criteria. Otherembodiments may limit the iterative process of steps 420-480 to acertain maximum number of iterations, to a certain duration of time, toa certain amount of usage of virtualized resources, or to combinationsthereof. If, for example, an embodiment limits the iterative process to1000 iterations, the iterative process will end after 1000 loops,regardless of whether an optimal configuration has been identified, andmay return at that time the fittest configuration that it has been ableto identify in any prior iteration, or the fittest configuration that ithas identified in the most recent iteration.

In embodiments that are not constrained in these ways, the iterativeprocess of steps 400-470 may terminate only when two or more successivegenerations of candidate configurations are seen to have converged to anoptimal state. This may be determined by means ofimplementation-dependent criteria and may, for example, occur when someor all chromosomes of two or more successive generations are identicalwithin a tolerance range (discounting the effect of mutation); or when asingle generation comprises candidate configurations of a singleresource instance 240/organism 340 that are identical within a tolerancerange (discounting the effect of mutation).

Embodiments that optimize a platform 210 may identify an optimalconvergence only when candidate configurations of the platform 210 as awhole converge. If, for example, configurations of a Web server instance340 comprised by the platform 210 converges but the platform 210 itselfdoes not, then convergence has not occurred. On the other hand, aconvergence termination condition may be deemed to have occurred whencandidate configurations of the platform configuration 210 converge,even if candidate configurations of the Web server 340 do not converge.

In some embodiments, convergence of the platform 210 or of some or allof the components of the platform 210 may constitute a defaulttermination condition. Other termination conditions, such assatisfaction of some other secondary criteria or the completion of amaximum number of iterations, may be considered only when it isdetermined that, although some entities converge or are converging,other entities do not seem to be converging to an optimal configurationwith each successive generation.

FIG. 5 illustrates details of step 410 of FIG. 4, which describes amethod of generating a set of baseline configurations of components of acloud-computing environment in accordance with embodiments of thepresent invention. FIG. 5 comprises steps 500-550.

In step 400 of FIG. 4, one or more processors received extrinsicinformation about a computing or communications platform 210 that mayallow elements of the environment or platform 210 to be organized intoframeworks similar to those shown in FIG. 2 or FIG. 3, and that mayfurther identify user requirements, resource constraints, and otherinformation that allows the one or more processors to perform furthersteps of FIG. 4.

In step 410 of FIG. 4, the one or more processors use the extrinsic datareceived in step 400 to generate a first generation of baselineconfigurations of components of the environment or platform 210. Eachbaseline configuration is identifies a possible configuration of oneresource instance 240 of a resource class 230 and comprises a distinctset of values of a set of configurable parameters associated with thatresource class 230.

In one example, a platform 210 might comprise three classes 230 ofresources, one of which might be a resource class 230 of Apache TomCatWeb servers. Components of the platform 210 might comprise four resourceinstances 240 of this TomCat resource class 230, each of those fourinstances 240 would in turn comprise an instance of a TomCat code base250 associated with the TomCat resource class 230, and each instance ofthe TomCat code base 250 would in turn comprise all functional blocks260 associated with an instance of a Tomcat Web server. Each of thesefunctional blocks 260 would be associated with set of configurableparameters that may be loaded with a set of values that identify aconfiguration of that functional block in a particular TomCat instance240.

In this example, the one or more processors in step 410 might generate aset of baseline candidate resource configurations for each of the fourresource instances 240, where each generated baseline configurationidentifies a set of values of configurable parameters comprised by oneor more functional blocks 260 of one instance 240 of the TomCat resourceclass 230 comprised by platform 210. If, for example, it is deemed thatstep 410 should create a generation of baseline candidate resourceconfigurations that comprise 100 possible configurations for eachresource instance 240, the one or more processors in step 240 would instep 410 (by means of a procedure of FIG. 5) create an initialgeneration of 400 baseline resource configurations that are compliantwith a code base 250 of a TomCat Web server (100 for each of the fourTomCat instances 240). Each of these configurations would comprise apossible set of values of configurable parameters associated with TomCatWeb servers. Further steps of the present invention would spawn futuregenerations of configurations from this initial generation, analyzingconfigurations of each spawned generation in order to identify andselect one or more most nearly optimized configurations.

In some embodiments, a baseline resource configuration may specifyvalues of all configurable parameters comprised by all functional blocks260 of a resource instance 240. In others, a baseline resourceconfiguration may specify values of a subset of all configurableparameters comprised by all such functional blocks 260. In either case,a baseline resource configuration may omit values of configurableparameters that are not deemed to be sufficiently relevant to anoptimization procedure of the present invention, wherein this deemingmay be a function of extrinsic data, such as resource constraints oruser requirements, received in step 400, or may be a function of expertknowledge of a designer of the embodiment.

Translating this step into analogous terminology of the biological modelof FIG. 3, each baseline resource configuration generated in step 410,by means of a procedure of FIG. 5, comprises a phenotype of a genome 350(code base 250) of each organism 340 (resource instance 240), whereinthe genome 350 is specific to a species 330 (resource class 230), andwherein the phenotype comprises a listing of all genes (a set of valuesof all configurable parameters) comprised by chromosomes 360 (functionalblocks 260) of the genome 350 associated with this particular species330.

In step 500, the one or more processors determine whether a prioriextrinsic data received in step 400 comprises information sufficient toenable the processors to generate configurations as a function ofreal-world characteristics of platform 210. If the processors determinethat the extrinsic data is sufficient to support such a procedure, themethod of FIG. 5 continues with steps 520-540. If the data is determinedto be insufficient, the method of FIG. 5 instead continues with step510.

In step 510, the one or more processors, lacking sufficient extrinsicinformation to create configurations as a function of real-worldperformance, load, resource constraint, user-requirement, or otherreceived data, generates the configurations through a statistical,random, or other procedure.

In performing one of these other procedures, the one or more processorsmight, for example, randomly select a value of one or more configurableparameters, or might randomly select a value that falls within a rangethat is identified by the extrinsic information received in step 400 orthat is identified by means of expert knowledge possessed by a designer,implementer, or user of an embodiment of the present invention.

In other examples, a value of a parameter might be selected by means ofa statistical operation that may be a function of a known statisticaldistribution or procedure. Such an operation may, for example, be afunction of a mean of a known range of values, may be a function of aknown distribution pattern, such as a Bell Curve or Gaussiandistribution function or a Poisson distribution, or may be a variation,component, or corollary of an other mathematical theorem or procedureknown to those skilled in the art, such as an element of Holland'sschema theorem.

In other examples, a first value of a parameter might selected by anycombination of these or similar methods and assigned to one candidateconfiguration of a resource instance 240 and subsequent values, to beassigned to other candidate configurations of the same resource instance240, would then be selected as a function of the first value, or as afunction of a different combination of methods.

At the conclusion of step 510, the one or more processors will havecreated a set of configurations associated with each functional block260 comprised by platform 210. As described above, platform 210 willcomprise a distinct set of functional blocks 260 for each resourceinstance comprised by platform 210.

In one example, step 510 might generate ten candidate baselineconfigurations of each functional block 260 comprised by platform 210.If platform 210 comprises two resource classes 230, each of which isassociated with 25 functional blocks, and platform 210 comprises sixinstances of a first resource class of the two resource classes andthree instances of a second resource class of the two resource classes,then step 510 will generate:(10 sets of configurations)*(25 blocks associated with each resourceinstance)*(6 instances of the first resource class+2 instances of thesecond resource class)=10*8*25 functional blocks=2000 distinct configurations,

each of which identifies a set of values of configurable parametersassociated with one resource class 230. In step 550, as described below,these 2000 configurations might then be organized into eighty groups,where each group of the eighty groups identifies one of ten sets of allrelevant values associated with one of the resource instances 240 ofplatform 210.

At the conclusion of step 510, the method of FIG. 5 continues with step550, described below.

If the one or more processors in step 500 determine that a prioriextrinsic data received in step 400 comprises information sufficient toenable the processors to generate baseline resource configurations as afunction of real-world characteristics of platform 210, the method ofFIG. 5 proceeds from step 500 to step 520, skipping step 510.

In step 520, the one or more processors begin a process of creating ageneration of baseline configurations as a function of extrinsic datareceived in step 400. This process begins by sampling current or pastreal-world values of one or more configurable parameters of functionalblocks 260 of the platform 210. Here, those real-world values may beidentified as a function of the extrinsic data received in step 400. Inone example, the extrinsic data may comprise performance or system logsfrom which such values, or an aggregation, average, or other function ofsuch values, may be retrieved.

In other embodiments, a more complex or nuanced selection method,including methods based on functions or techniques known to thoseskilled in the art, may be performed. In one example that comprises avariation of the Holland schema theorem, the one or more processors maycreate a set of ten distinct configurations, or instances, of afunctional block 260, where each instance of the ten distinctconfigurations comprises a random set of values of configurableparameters identified by the block 260. The one or more processors wouldthen rank the ten randomly generated configurations by means of afitness function or other ranking mechanism.

In other embodiments, the one or more processors may in step 520 samplevalues of configurable parameters of a resource instance 240 from a setof real-world current or historical values culled from the extrinsicinformation received in step 400. The one or more processors might thenbuild each configuration as a function of the sampled values. Such afunction might comprise duplicating an existing real-world set ofvalues, duplicating a subset of such an existing set of values,generating new values by performing a statistical or other mathematicalfunction on the existing set of values, combining values generated byany of these means with randomly selected values, or a combination ofthese and other value-selection methods. In yet another embodiment, theone or more processors in step 520 might select values as a statisticalfunction of a distribution of values comprised by the extrinsic datareceived in step 400.

In step 530, the one or more processors stratify or rank the baselineconfigurations selected in step 520. This stratification might organizeor sort the configurations into percentile slots as a function of anoperation of a fitness function, of similarities among configurations,of a statistical operation known to those skilled in the art of dataanalysis, or of a combination of any of these or other appropriatedata-analysis techniques.

In one embodiment, a designated number of configurations might beselected randomly, or as designated by an implementation-dependentfunction, from each of a set of percentile ranges. In one example, if1000 configurations are ranked into ten percentile ranges, each of whichspans a 10% range of relative fitness of a ranked configuration, the oneor more processors may in step 530 select 10% of the configurations ineach range, or may select ten configurations from each range. Many otherselection methods are possible, some of which are known to those skilledin the art and some of which are a function of implementation-dependentcriteria.

In some embodiments, specific configurations, configurable parameters,resource classes, resource instances, functional blocks, or otherentities may be weighted to represent their relative importance orrelevance. This weighting may be configured, selected, or implemented asa function of either statistical functions known to those skilled in theart, expert knowledge of the platform 210, or extrinsic informationreceived in step 400.

In step 540, the one or more processors selects a final set of baselineconfigurations from the configurations chosen in step 530. This finalselection may be a function of a confidence interval identified byexpert knowledge of a system designer or by user requirements comprisedby the extrinsic information received in step 400.

In one example, if a 95% confidence interval is desired, the one or moreprocessors may achieve this goal by means of a statistical method knownto those skilled in the art and based on a mean and standard deviationof real-world values comprised by the extrinsic information or based ona mean and standard deviation of the configurations generated in step530, based on. Such a statistical method would determine a number ofconfigurations that must be selected in order to ensure, with 95%confidence, that the number of configurations is likely to provide anaccurate representation of real-world values of correspondingconfigurable parameters of the platform 210.

At the conclusion of step 540, a set of baseline configurations willhave been selected for each resource instance 240/organism 340, whereeach baseline configuration of a resource instance 240 will comprise aset of values of configurable parameters associated with that resourceinstance 240. In embodiments in which all the functional blockscomprised by the resource instance 240 have been combined prior to thisstep (rather than below, in step 550), each baseline configuration ofthe resource instance 240 will comprise values of all relevantconfigurable parameters associated with the resource instance 240.

In the parlance of the biological model of FIG. 3, at the conclusion ofstep 540, the one or more processors will have generated a set ofbaseline phenotypes configuration of each chromosome 360 of eachorganism 340 of the ecosystem 310. In embodiments in which all thechromosomes of the organism 340 have been combined prior to this step(rather than below, in step 550), into an instance of a genome 350 ofthe organism 340, each baseline phenotype of the organism 340 willcomprise a phenotype of the entire genome 350 of the organism 340.

In step 550, instances of functional blocks 260 generated by a method ofFIG. 5 may be organized into groups that each comprise one populated setof all functional blocks 260 comprised by one resource instance 240. Ina process analogous to a ligasing (or joining) of a set of chromosomesinto a single larger molecule, the one or more processors here combine,append, merge, join, or otherwise associate functional blocks 260 toform a set of complete resource configurations that each represent acandidate resource configuration of one resource instance 240.

In embodiments wherein steps 510-540 have generated multiple instancesof each functional block 260, each resource configuration may be formedfrom a group of functional blocks 260 that share an association. Such anassociation may, for example, be identified when the functional blocks260 are generated in steps 510-540, as a function of a method ofgeneration.

In other embodiments, functional blocks 260 generated in steps 510-540may be mixed and matched in step 550 to generate configurations that mayshare some same functional blocks in different combinations.

In one example, if a resource class 230 is associated with sixtyfunctional blocks 260, steps 510-540 may generate ten instances of eachblock 260 as components of ten baseline configurations of a resourceinstance 240 of the resource class 230. Here, each instance isassociated with one set often distinct sets of the sixty blocks 260, andeach set is associated with one configuration of the resource instance240.

This first method would produce ten complete baseline resourceconfigurations if the processors in step 550 simply ligase each distinctset of blocks 260 into one configuration. However, if the processors instep 550 are free to generate a baseline resource configuration thatcombines blocks 260 associated with more than one set of the tendistinct sets, then many thousands of configurations are possible ascombinations of the six hundred generated blocks 260.

In embodiments that comprise this second method, a method of selectingeach combination of blocks may be configured by any means known to thoseskilled in the art of statistical modeling, information technology, orother fields. Blocks may be selected by a method that comprises one ormore random selections, one or more selections based on statisticaldistributions of values of each configurable parameter, or one or moreselections that consider factors such as a value's distance from a mean,a number of instances of configurable parameters that comprise identicalvalues, and so forth.

At the conclusion of step 550, the one or more processors will haveassembled a set of baseline resource configurations for each resourceinstance 240 of the platform 210. Each set may comprise a number ofconfigurations that is determined as a function of extrinsic informationreceived in step 400, that may be a function of the size or number ofone or more elements represented in the model of FIG. 2, or of otherimplementation-dependent factors.

Each configuration of such a set of baseline resource configurationsidentifies a set of values of all configurable parameters associatedwith one resource instance 240 of the platform 210. In some embodiments,every configuration associated with a particular resource instance 240must identify a distinct and unique combination of values. In someembodiments, no two configurations associated with a particular resourceinstance 240 may comprise identical values of a same configurableparameter. In other embodiments, values identified by each configurationmay be subject to other constraints, or may be permitted to assume anyvalues within a range of values identified by the extrinsic informationreceived in step 400.

The one or more processors will then, at the conclusion of the method ofFIG. 5, perform step 420 of FIG. 4, which will initiate a firstiteration of the iterative process of steps 420-480, and wherein thisfirst iteration will evaluate one set of baseline resourceconfigurations generated by the method of FIG. 5 for each resourceinstance 240 of platform 210.

In some embodiments, the procedure of FIG. 5 may further produce, in asimilar manner, a set of baseline environment configurations, each ofwhich comprises a set of values of configurable parameters associatedwith the entire platform 210. These baseline environment configurationsmay be generated from the ground up, by generating distinct instances offunctional blocks 260 associated with relevant performance, efficiency,or other characteristics of the platform as a whole. In other cases,each baseline environment configuration may be assembled in step 550 byjoining (or “ligasing”) instances of functional blocks generated by amethod of FIG. 5, by joining (or “ligasing”) baseline resourceconfigurations generated by a method of FIG. 5, or by a combination ofthe two techniques.

In some embodiments, the procedure of step 550 may be performed at adifferent sequential point of a method of FIG. 4 or FIG. 5. In oneexample, instead of performing a step 510 or steps 520-540 on discreteor isolated functional blocks 26, step 510 or steps 520-540 may insteadbe performed on sets of functional blocks 260 that have already beenjoined by a procedure similar to that of step 550 described above.

In some embodiments, a joining or ligasing function may comprise acreation of a polynomial function in which each term or coefficient ofthe polynomial represents a value of one configurable parameter or otherinformation stored in one functional block 260 or in some other logicalentity comprised by a resource instance 240. Such polynomialrepresentations may be associated with one or more polynomial basedfitness functions described above.

What is claimed is:
 1. A method for optimizing a virtualized computingenvironment, the method comprising: a processor of a computer systemreceiving extrinsic data that describes a set of virtual resourcescomprised by the cloud-computing environment; the processor selecting aset of candidate resource configurations, where every virtual resourceof the set of virtual resources is associated with a plurality ofcandidate resource configurations of the set of candidate resourceconfigurations; the processor further selecting a set of candidateenvironment configurations, where each candidate environmentconfiguration of the set of candidate environment configurationscomprises at least one candidate resource configuration of the set ofcandidate resource configurations; the processor provisioning a virtualtest environment that simulates the cloud-computing environment, andthat comprises a virtual representation of each resource of the set ofvirtual resources; the processor configuring the virtual testenvironment by successively loading the virtual test environment withconfiguration combinations, where each combination of the configurationcombinations comprises at least two candidate environment configurationsof the set of candidate environment configurations and at least twocandidate resource configurations of the set of candidate resourceconfigurations, and where the at least two candidate resourceconfigurations are each associated with a same tested resource of theset of virtual resources; the processor ranking the at least twocandidate resource configurations and the at least two candidateenvironment configurations; the processor revising at least onecandidate resource configuration of the set of candidate resourceconfigurations and at least one candidate environment configuration ofthe set of candidate environment configurations as a function of theranking; and the processor repeating the configuring, the ranking, andthe revising until a resulting virtual test environment is an optimalvirtual environment.
 2. The method of claim 1, further comprising: theprocessor evaluating a first resource-fitness characteristic of a firstloaded resource configuration of the at least two candidate resourceconfigurations, where the first resource-fitness characteristic isassigned a value as a function of an operation of the same testedresource within the virtual test environment; the processor deriving avalue of an environment-fitness characteristic of a first loadedenvironment configuration of the at least two candidate environmentconfigurations, where the deriving is performed as a function of anoperation of the virtual test environment while the virtual testenvironment is loaded with the first loaded environment configuration;and the processor performing the ranking as a function of the evaluatingand the deriving.
 3. The method of claim 1, where a first virtualresource of the set of virtual resources comprises a first set offunctional blocks, where a first functional block of the first set offunctional blocks comprises a first set of configurable parameters, andwhere a first configuration of the first virtual resource comprises afirst set of values of the first set of configurable parameters.
 4. Themethod of claim 3, further comprising: the processor organizing the setof virtual resources, the functional blocks comprised by each virtualresource of the set of virtual resources, and the sets of configurableparameters associated with each virtual resource of the set of virtualresources into a hierarchical structure, where the organizing isperformed as a function of the received extrinsic data.
 5. The method ofclaim 1, where the virtual test environment is provisioned as a functionof resource constraints, projected loads, and user requirements furtherdescribed by the received extrinsic data.
 6. The method of claim 2,further comprising: the processor identifying a plurality of distinctsubsets of a union of the at least two candidate environmentconfigurations and the at least two candidate resource configurations;and the processor performing multiple iterations of the loading,evaluating, and deriving, where each iteration of the multipleiterations comprises loading the virtual test environment with adifferent subset of the plurality of distinct subsets.
 7. The method ofclaim 3, further comprising: the processor organizing the set of allfunctional blocks comprised by all virtual resources of the set ofvirtual resources into a set of groups, where a first group of the setof groups comprises all functional blocks that comprise one or moreconfigurable parameters associated with a first resource of the set ofvirtual resources, where a configuration of the set of first-resourceconfigurations, of the set of candidate resource configurations, thatidentifies a configuration of the first resource and comprises the firstgroup of the set of groups, and where a distinct candidate configurationof the first resource is identified by a distinct set of one or morevalues of the one or more configurable parameters comprised by aresource configuration of the set of first-resource configurations. 8.The method of claim 3, where revising a first candidate resourceconfiguration comprises revising one or more values of one or moreconfigurable parameters comprised by the first candidate configuration,where the first candidate resource configuration is selected from theset of candidate resource configurations by an application of a firststatistical function, where the one or more configurable parameters areselected from a set of all configurable parameters comprised by thefirst candidate resource configuration by an application of a secondstatistical function, and where the revised one or more values areselected by an application of a third statistical function.
 9. Themethod of claim 8, where the first, second, or third statisticalfunction comprises a random selection.
 10. The method of claim 3, wherethe revising at least one candidate resource configuration comprisesproducing and adding two new configurations to the set of candidateresource configurations by crossing two existing configurations of theset of candidate resource configurations, where the two existingconfigurations each identify a distinct configuration of a samecrossover resource of the set of virtual resources, and where crossing afirst configuration of the two existing configurations with a secondconfiguration of the two existing configurations comprises: theprocessor selecting one or more configurable parameters common to bothof the two existing configurations; and the processor swapping a firstset of values of the selected one or more configurable parameterscomprised by the first configuration with a second set of values of theselected one or more configurable parameters comprised by the secondconfiguration, where the two existing configurations are selected fromthe set of candidate resource configurations by an application of afirst statistical function, and where the selected one or moreconfigurable parameters are selected from a set of all configurableparameters comprised by the two existing configurations by anapplication of a second statistical function.
 11. The method of claim10, further comprising: the processor determining that the virtual testenvironment is an optimal virtual environment by identifying that a mostrecent repetition of the configuring, the ranking, and the revisingsubstantially alters neither the set of candidate resourceconfigurations nor the set of candidate environment configurations,where the substantially altering comprises producing a difference in oneor more values of one or more configurable parameters comprised by theset of candidate resource configurations or by the set of set ofcandidate environment configurations, such that the produced differenceexceeds a predetermined threshold level.
 12. The method of claim 1,where the termination condition is selected from the group consistingof: consuming an amount or number of resources that exceeds apredetermined threshold level, exceeding a time limit, and repeating theprovisioning, the ranking, and the revising.
 13. The method of claim 1,further comprising providing at least one support service for at leastone of creating, integrating, hosting, maintaining, and deployingcomputer-readable program code in the computer system, where thecomputer-readable program code in combination with the computer systemis configured to implement the receiving, selecting, further selecting,provisioning, configuring, ranking, revising, and repeating.
 14. Acomputer program product, comprising a computer-readable hardwarestorage device having a computer-readable program code stored therein,said program code configured to be executed by a processor of a computersystem to implement a method for optimizing a virtualized computingenvironment, the method comprising: a processor of a computer systemreceiving extrinsic data that describes a set of virtual resourcescomprised by the cloud-computing environment; the processor selecting aset of candidate resource configurations, where every virtual resourceof the set of virtual resources is associated with a plurality ofcandidate resource configurations of the set of candidate resourceconfigurations; the processor further selecting a set of candidateenvironment configurations, where each candidate environmentconfiguration of the set of candidate environment configurationscomprises at least one candidate resource configuration of the set ofcandidate resource configurations; the processor provisioning a virtualtest environment that simulates the cloud-computing environment, andthat comprises a virtual representation of each resource of the set ofvirtual resources; the processor configuring the virtual testenvironment by successively loading the virtual test environment withconfiguration combinations, where each combination of the configurationcombinations comprises at least two candidate environment configurationsof the set of candidate environment configurations and at least twocandidate resource configurations of the set of candidate resourceconfigurations, and where the at least two candidate resourceconfigurations are each associated with a same tested resource of theset of virtual resources; the processor ranking the at least twocandidate resource configurations and the at least two candidateenvironment configurations; the processor revising at least onecandidate resource configuration of the set of candidate resourceconfigurations and at least one candidate environment configuration ofthe set of candidate environment configurations as a function of theranking; and the processor repeating the configuring, the ranking, andthe revising until a resulting virtual test environment is an optimalvirtual environment.
 15. The computer program product of claim 14,further comprising: the processor evaluating a first resource-fitnesscharacteristic of a first loaded resource configuration of the at leasttwo candidate resource configurations, where the first resource-fitnesscharacteristic is assigned a value as a function of an operation of thesame tested resource within the virtual test environment; the processorderiving a value of an environment-fitness characteristic of a firstloaded environment configuration of the at least two candidateenvironment configurations, where the deriving is performed as afunction of an operation of the virtual test environment while thevirtual test environment is loaded with the first loaded environmentconfiguration; and the processor performing the ranking as a function ofthe evaluating and the deriving.
 16. The computer program product ofclaim 14, where a first virtual resource of the set of virtual resourcescomprises a first set of functional blocks, where a first functionalblock of the first set of functional blocks comprises a first set ofconfigurable parameters, and where a first configuration of the firstvirtual resource comprises a first set of values of the first set ofconfigurable parameters.
 17. The computer program product of claim 14,further comprising: the processor determining that the virtual testenvironment is an optimal virtual environment by identifying that a mostrecent repetition of the configuring, the ranking, and the revisingsubstantially alters neither the set of candidate resourceconfigurations nor the set of candidate environment configurations,where the substantially altering comprises producing a difference in oneor more values of one or more configurable parameters comprised by theset of candidate resource configurations or by the set of set ofcandidate environment configurations, such that the produced differenceexceeds a predetermined threshold level.
 18. A computer systemcomprising a processor, a memory coupled to said processor, and acomputer-readable hardware storage device coupled to said processor,said storage device containing program code configured to be run by saidprocessor via the memory to implement a method for optimizing avirtualized computing environment, the method comprising: the processorof the system receiving extrinsic data that describes a set of virtualresources comprised by the cloud-computing environment; the processorselecting a set of candidate resource configurations, where everyvirtual resource of the set of virtual resources is associated with aplurality of candidate resource configurations of the set of candidateresource configurations; the processor further selecting a set ofcandidate environment configurations, where each candidate environmentconfiguration of the set of candidate environment configurationscomprises at least one candidate resource configuration of the set ofcandidate resource configurations; the processor provisioning a virtualtest environment that simulates the cloud-computing environment, andthat comprises a virtual representation of each resource of the set ofvirtual resources; the processor configuring the virtual testenvironment by successively loading the virtual test environment withconfiguration combinations, where each combination of the configurationcombinations comprises at least two candidate environment configurationsof the set of candidate environment configurations and at least twocandidate resource configurations of the set of candidate resourceconfigurations, and where the at least two candidate resourceconfigurations are each associated with a same tested resource of theset of virtual resources; the processor ranking the at least twocandidate resource configurations and the at least two candidateenvironment configurations; the processor revising at least onecandidate resource configuration of the set of candidate resourceconfigurations and at least one candidate environment configuration ofthe set of candidate environment configurations as a function of theranking; and the processor repeating the configuring, the ranking, andthe revising until a resulting virtual test environment is an optimalvirtual environment.
 19. The computer system of claim 18, furthercomprising: the processor evaluating a first resource-fitnesscharacteristic of a first loaded resource configuration of the at leasttwo candidate resource configurations, where the first resource-fitnesscharacteristic is assigned a value as a function of an operation of thesame tested resource within the virtual test environment; the processorderiving a value of an environment-fitness characteristic of a firstloaded environment configuration of the at least two candidateenvironment configurations, where the deriving is performed as afunction of an operation of the virtual test environment while thevirtual test environment is loaded with the first loaded environmentconfiguration; and the processor performing the ranking as a function ofthe evaluating and the deriving, where a first virtual resource of theset of virtual resources comprises a first set of functional blocks,where a first functional block of the first set of functional blockscomprises a first set of configurable parameters, and where a firstconfiguration of the first virtual resource comprises a first set ofvalues of the first set of configurable parameters.
 20. The computersystem of claim 18, further comprising: the processor determining thatthe virtual test environment is an optimal virtual environment byidentifying that a most recent repetition of the configuring, theranking, and the revising substantially alters neither the set ofcandidate resource configurations nor the set of candidate environmentconfigurations, where the substantially altering comprises producing adifference in one or more values of one or more configurable parameterscomprised by the set of candidate resource configurations or by the setof set of candidate environment configurations, such that the produceddifference exceeds a predetermined threshold level.